Function reference • segclust2d

All functions
`DynProg()`	DynProg computes the change points given a cost matrix matD and a maximum number of segments Kmax
`DynProg_algo_cpp()`	DynProg_algo_cpp
`EM.algo_simultanee()`	EM.algo_simultanee calculates the MLE of phi for given change-point instants
`EM.algo_simultanee_Cpp()`	EM.algo_simultanee calculates the MLE of phi for given change-point instants and for a fixed number of clusters
`EM.init_simultanee()`	EM.init_simultanee proposes an initial value for the EM algorithm based on a hierarchical clustering algorithm (ascending)
`Estep_simultanee()`	Estep_simultanee computes posterior probabilities and incomplete-data log-likelihood for mixture models
`Gmean_simultanee()`	Gmean_simultanee calculates the cost matrix for a segmentation model with changes in the mean and variance for all signals
`Gmixt_algo_cpp()`	Gmixt_algo_cpp
`Gmixt_simultanee()`	Gmixt_simultanee calculates the cost matrix for a segmentation/clustering model
`Gmixt_simultanee_fullcpp()`	Gmixt_simultanee_fullcpp
`Mstep_simultanee()`	Mstep_simultanee computes the MLE within the EM framework
`Mstep_simultanee_cpp()`	Mstep_simultanee computes the MLE within the EM framework
`add_covariates()`	Covariate Calculations
`angular_speed()`	Calculate angular speed along a path
`apply_rowSums()`	apply_rowSums
`apply_subsampling()`	Internal function for subsampling
`argcheck_Kmax()`	Check for argument 'Kmax'
`argcheck_diag.var()`	Check for argument 'diag.var'
`argcheck_lmin()`	Check for argument 'lmin'
`argcheck_ncluster()`	Check for argument 'ncluster'
`argcheck_order.var()`	Check for argument 'order.var'
`argcheck_ordering()`	Check for argument 'order'
`argcheck_scale.variable()`	Check for argument 'scale.variable'
`argcheck_seg.var()`	Check for argument 'seg.var'
`argcheck_segclust()`	Check for argument 'ncluster' and 'nseg'
`argcheck_segmentation()`	Check for argument 'nseg'
`argcheck_type_coord()`	Check for deprecated 'type' and 'coord.names' argument
`arma_repmat()`	arma_repmat
`augment()`	Generic function for augment
`bisig_plot()`	bisig_plot draws the plots of the bivariate signal on the same plot (scale free)
`calc_BIC()`	Calculate BIC
`calc_dist()`	Calculate distance between locations
`calc_speed()`	Calculate speed along a path
`calc_stat_states()`	Calculate state statistics
`check_repetition()`	Check for repetition in the series
`choose_kmax()`	Finding best segmentation with a different threshold S
`chooseseg_lavielle()`	Internal Function for choosing optimal number of segment
`colsums_sapply()`	colsums_sapply
`cumsum_cpp()`	cumsum_cpp
`find_mu_sd()`	Find mean and standard deviation of segments
`hybrid_simultanee()`	`hybrid_simultanee` performs a simultaneous seg - clustering for bivariate signals.
`initialisePhi()`	initialisePhi is the constructor for a set of parameters for a segclust model
`likelihood()`	Generic function for likelihood
`logdens_simultanee_cpp()` `logdens_simultanee()`	logdens_simultanee_cpp
`map_segm()`	`plot_segm` plot segmented movement data on a map.
`matrixRupt()`	matrixRupt transforms a vector of change point into a data.frame with start and end of every segment
`neighborsbis()`	neighbors tests whether neighbors of point k,P can be used to re-initialize the EM algorithm and to improve the log-likelihood.
`plot_segm()`	Plot segmentation on time-serie
`plot_states()`	Plot states statistics
`prep_segm()`	Find segment and states for a Picard model
`prep_segm_HMM()`	Internal function for HMM
`prep_segm_shiftfit()`	Internal function for HMM
`prepare_HMM()`	Prepare HMM output for proper comparison plots
`prepare_shiftfit()`	Prepare shiftfit output for proper comparison plots
`relabel_states()`	Relabel states of a segmentation/clustering output
`repmat()`	repmat repeats a matrix
`ruptAsMat()`	ruptAsMat is an internal function to transform a vector giving the change point to matrix 2 columns matrix in which each line gives the beginning and the end of a segment
`segclust()`	Segmentation/Clustering of movement data - Generic function
`segclust2d`	segclust2d: tools for segmentation of animal GPS movement data
`segclust_internal()`	Internal segmentation/clustering function
`segmap_list()`	`segmap_list` create maps with a list of object of `segmentation` class
`print(<segmentation>)` `plot(<segmentation>)` `likelihood(<segmentation>)` `plot_likelihood()` `get_likelihood()` `logLik(<segmentation>)` `plot_BIC()` `BIC(<segmentation>)` `stateplot()` `states()` `segment()` `augment(<segmentation>)` `segmap()`	segmentation class description
`segmentation()` `segmentation_internal()`	Segmentation of movement data - Generic function
`simulmode`	Simulations of behavioural mode
`simulshift`	Simulations of home-range shift
`spatial_angle()`	Calculate spatial angle along a path
`stat_segm()`	Calculate statistics on a given segmentation
`stat_segm_HMM()`	Get segment statistic for HMM model
`stat_segm_shiftfit()`	Get segment statistic for shiftfit model
`subsample_rename()`	Internal function for subsampling
`test_data()`	Test function generating fake data
`wrap_dynprog_cpp()`	DynProg Rcpp DynProg computes the change points given a cost matrix matD and a maximum number of segments Kmax

Reference

All functions